Indian Rocks, PA
Indian Rocks wildfire risk explained
USFS's Wildfire Risk to Communities model puts Indian Rocks at the 46th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 361 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Indian Rocks at the 48th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Indian Rocks's buildings actually sit
100% of Indian Rocks's 361 buildings sit in USFS's Direct exposure zone, roughly 361 structures close enough to burnable vegetation for flame contact, not just embers — 0% fall in the Indirect, ember-only zone and 0% are Minimal.
How Indian Rocks compares
Compare Indian Rocks's two percentiles: 75th within Pennsylvania, only 46th nationally — a gap of 28 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Indian Rocks ranks 16,906 for wildfire risk (1 is highest) and 20,048 by building count (1 is largest). Within Pennsylvania alone, it ranks 505 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Shopping for coverage in Indian Rocks
Indian Rocks's elevated wildfire rating (46th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Lowering exposure, not just insuring around it
Because Direct exposure dominates in Indian Rocks (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Indian Rocks's figures come from
Every one of the two percentiles behind Indian Rocks's 16,906-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Indian Rocks's dominant direct exposure actually means, with real examples from across the dataset.